Innate immunocompetent iNSpheroids: A hiPSC-derived 3D model to study the central nervous system captures an early CNS response to rAAV
Bibliographic record
Abstract
Abstract Gene therapies using adeno-associated viruses (AAVs) for central nervous system (CNS) disorders face challenges due to host immune responses not represented in classical preclinical models. Here, we present a human-induced pluripotent stem cell (hiPSC)-derived innate immunocompetent 3D CNS model that recapitulates neuroinflammatory hallmarks, serving as a platform for preclinical gene therapy development. Utilizing various scales of stirred-tank bioreactor systems, we generated (neurospheroids) iNSpheroids composed of neurons, astrocytes, and oligodendrocytes, alongside microglial cells (iMGL) to mimic the neuro-immune axis. These systems enabled large-scale production of iNSpheroids and subsequent miniaturization for co-culture experiments and screening of inflammatory stimuli, while maintaining a highly controlled environment. The iMGL-iNSpheroids demonstrated active neuron-microglia crosstalk and exhibited distinct inflammatory responses to a series of neuroinflammatory factors. iMGL-iNSpheroids mounted a mild and transient response to rAAV9, mediated by the activation of inflammatory pathways (e.g., TNF-via NF-κB activation) in glial cell populations. This model offers a valuable tool to dissect neuroinflammatory mechanisms, accelerating gene therapy development. Teaser Immune-competent 3D human CNS model recapitulates glial responses to rAAVs, enabling reliable preclinical gene therapy screening.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".